A Comparison of Classification Strategies in Genetic Programming with Unbalanced Data

@InProceedings{conf/ausai/BhowanZJ10,
title = "A Comparison of Classification Strategies in Genetic
Programming with Unbalanced Data",
author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
booktitle = "Australasian Conference on Artificial Intelligence",
editor = "Jiuyong Li",
year = "2010",
volume = "6464",
series = "Lecture Notes in Computer Science",
pages = "243--252",
address = "Adelaide",
month = dec,
publisher = "Springer",
bibdate = "2010-11-30",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ausai/ausai2010.html#BhowanZJ10",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-17431-5",
DOI = "doi:10.1007/978-3-642-17432-2_25",
size = "10 pages",
abstract = "Machine learning algorithms like Genetic Programming
(GP) can evolve biased classifiers when data sets are
unbalanced. In this paper we compare the effectiveness
of two GP classification strategies. The first uses the
standard (zero) class-threshold, while the second uses
the best class-threshold determined dynamically on a
solution-by-solution basis during evolution. These two
strategies are evaluated using five different GP
fitness across across a range of binary class imbalance
problems, and the GP approaches are compared to other
popular learning algorithms, namely, Naive Bayes and
Support Vector Machines. Our results suggest that there
is no overall difference between the two strategies,
and that both strategies can evolve good solutions in
binary classification when used in combination with an
effective fitness function.",
affiliation = "School of Engineering and Computer Science, Victoria
University of Wellington, New Zealand",
}